CIF: Small: Learning, Optimization & Analysis for Biologically Inspired Community Networks

细菌是单细胞有机体，构成了一些最早的生命形式。细菌的总生物量比所有动植物的生物量总和还要大。尽管它们很简单，但细菌的运作和相互作用尚不完全清楚，但微生物群落在生物修复、植物生长促进、人和动物消化、疾病、碳循环和水的净化方面发挥着重要作用。因此，进一步了解微生物种群具有重要意义。该项目使用通信和信号处理的方法来建模、优化并有望进一步了解微生物种群内的相互作用。特别关注的是群体感应现象，在这种现象中，细菌在正确的环境和种群条件下表达新的基因，这有助于生物膜形成等集体行为以及感染等毒力行为。因此，群体感应使这些简单的单细胞生物能够完成复杂的协作任务。该项目将培训研究生进行跨学科研究，并可能影响微生物燃料电池、细菌感染抑制、商业蜂窝系统、搜救、交通系统、环境监测和国土安全等应用和行业。将考虑生物启发问题的建模、学习和优化策略。将直接解决几个独特的挑战：(1)开发能够进行优化和控制的信号和通道模型，同时捕获细菌的关键操作行为；(2)设计与微生物应用相关的有意义的成本函数；以及(3)设计解决单个细菌之间以及细菌与其环境之间的内在耦合的模型。该项目将群体感应视为多代理、分布式决策系统的关键要素，即细菌群落。一个关键的挑战是将一切结合在一起：纳入生物约束、微生物之间的耦合、在微生物群体的时间和空间演变的背景下确定适当的目标函数以捕捉生物适宜性或其他微生物目标的挑战。将利用以下工具：分散检测/决策、顺序决策、主动定位、主动抽样、随机几何、因果图识别、有向信息、双向通信、多跳通信和分组调度方法。虽然拟议的工作没有重要的实验内容，但将利用先前在现有合作背景下收集的实验数据来推动拟议的研究。该项目旨在闭合循环，不仅使用通信、信号处理和信息理论来模拟和设计微生物系统，而且使用这一生物启发研究的结果来设计工程系统。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Bacteria are single-celled organisms that constitute some of the earliest forms of life. Bacteria have an aggregate biomass that is larger than that of all animals and plants combined. Despite their simplicity, the operations and interactions of bacteria are not fully understood, yet microbial communities play a significant role in bioremediation, plant growth promotion, human and animal digestion, disease, the carbon-cycle and the cleaning of water. Thus, it is of significant interest to further understand microbial populations. This project uses methods from communications and signal processing to model, optimize and hopefully further understand the interactions within microbial populations. The particular focus is on the phenomenon of quorum sensing wherein bacteria express new genes, under the right environmental and population conditions, which facilitates collective behavior such as biofilm formation as well as virulent behavior such as infections. Thus, quorum sensing enables these simple unicellular organisms to achieve complex, collaborative tasks. The project will train graduate students in cross-disciplinary research and potentially impact applications and industries such as microbial fuel cells, bacterial infection suppression, commercial cellular systems, search-and-rescue, transportation systems, environmental monitoring, and homeland security. Modeling, learning and optimization strategies for biologically inspired problems will be considered. Several unique challenges will be directly addressed: (1) developing signaling and channel models that enable optimization and control, while capturing key operational behavior of the bacteria; (2) designing meaningful cost functions that are relevant to the microbial application; and (3) designing models addressing the inherent coupling between individual bacteria as well as coupling between the bacteria and their environment. The project views quorum sensing as a critical element of a multi-agent, distributed decision-making system that is the bacterial community. A key challenge is to bring everything together: the incorporation of biological constraints, the coupling between microbes, the challenge of determining the proper objective function that captures biological fitness or other microbial objectives in the context of temporal and spatial evolution of microbial colonies. The following tools will be leveraged: decentralized detection/decision making, sequential decision making, active localization, active sampling, stochastic geometry, causal graph identification, directed information, two-way communication, multi-hopped communication, and packet scheduling methodologies. While the proposed work does not have a significant experimental content, previously collected experimental data in the context of existing collaborations will be leveraged to drive the proposed research. The project aims to close the loop by not only using communication, signal processing and information theory to model and design microbial systems, but to use the outcomes of this biologically inspired research to design engineered systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.